code: def mean_and_std(
      values: Sequence[types.MetricValueType], ddof: int
  ) -> Tuple[types.MetricValueType, types.MetricValueType]:
      """Computes mean and standard deviation for (structued) metric values.
  
      Args:
        values: An iterable of values for which to compute the mean and standard
          deviation
        ddof: The difference in degrees of freedom to use for the standard deviation
          computation, relative to the length of values. For example, if len(values)
          == 10, and ddof is 1, the standard deviation will be computed with 9
          degreees of freedom
  
      Returns:
         A 2-tuple in which the first element is the mean and the second element is
         the standard deviation. The types of the mean and standard deviation will
         be the same as the type of each element in values.
      """
      total = None
      for value in values:
          if total is None:
              total = value
          else:
              total = total + value
      mean = total / len(values)
      squared_residual_total = None
      for value in values:
          squared_residual = (value - mean) ** 2
          if squared_residual_total is None:
              squared_residual_total = squared_residual
          else:
              squared_residual_total = (
                  squared_residual_total + squared_residual
              )
      std = (squared_residual_total / (len(values) - ddof)) ** 0.5
      return mean, std
  
file_path: model-analysis/tensorflow_model_analysis/evaluators/confidence_intervals_util.py
imports: import collections
  import numbers
  from typing import Iterable, NamedTuple, Optional, Sequence, Set, Tuple
  import apache_beam
  import numpy
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.post_export_metrics import metric_keys
label_desc: Use image node_id 1 for calling a global function with example usage: mean_and_std(values, ddof) and returns: mean,  std